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Identifying good practices for detecting inter-regional linear functional connectivity from EEG.

Franziska Pellegrini1, Arnaud Delorme2, Vadim Nikulin3

  • 1Charité-Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany; Bernstein Center for Computational Neuroscience, Philippstraße 13, Berlin, 10117, Germany.

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Summary

This study compares methods for analyzing brain functional connectivity (FC) using simulated data. The best pipelines for estimating phase-to-phase FC involve LCMV beamforming, PCA, and specific connectivity metrics like MIM or TRGC.

Keywords:
ElectroencephalographyInter-regional functional connectivityLinearly-constrained minimum variance beamformingMultivariate interaction measureSimulationSource reconstructionTime-reversed granger causality

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Characterizing functional connectivity (FC) between brain regions is crucial for understanding brain function.
  • Aggregating voxel-level statistical dependencies into inter-regional FC involves various methods with unclear advantages.

Purpose of the Study:

  • To compare the performance of different pipelines for estimating directed and undirected linear phase-to-phase FC.
  • To identify optimal methods for aggregating voxel-level data into inter-regional FC metrics.
  • To investigate factors influencing the detection of phase-to-phase FC.

Main Methods:

  • Generation of ground-truth data for simulated pseudo-EEG.
  • Comparison of inverse modeling algorithms, time series aggregation strategies, and connectivity metrics.
  • Evaluation of pipelines involving linearly-constrained minimum variance (LCMV) beamformer, principal component analysis (PCA), multivariate interaction measure (MIM), and time-reversed Granger Causality (TRGC).

Main Results:

  • Pipelines using the absolute value of coherency performed poorly.
  • Combining dynamic imaging of coherent sources (DICS) beamforming with multi-frequency directed FC metrics yielded unsatisfactory results.
  • Pipelines involving LCMV beamforming, PCA, and MIM/TRGC showed promising performance.

Conclusions:

  • Recommendations are provided to enhance the validity of future experimental connectivity studies.
  • The free ROIconnect plugin for EEGLAB is introduced, incorporating recommended methods.
  • The best-performing pipeline was demonstrated on EEG data during motor imagery.